Ocean Worlds represent one of the best chances for extra-terrestrial life in our solar system. A new mission concept must be developed to explore these oceans. This mission would require traversing the 10s of km thick icy shell and releasing a submersible into the ocean below. During the transit of the icy shell and the exploration of the ocean, the vehicle(s) would be out of contact with Earth for weeks or potentially months at a time. During this time the vehicle must have sufficient autonomy to locate and study scientific targets of interest. One such target of interest is hydrothermal venting. We have previously developed an autonomous nested search method to locate and investigate sources of hydrothermal venting by locating local maxima in hydrothermal vent emissions. In this work we demonstrate this approach on board an OceanServer Iver2 AUV in Chesapeake Bay, MD using simulated sensor data from a hydrothermal plume model. This represents the first step towards the deployment of this approach in conditions analogous to those that we might expect on an Ocean World.

This paper addresses the problem of ice-relative navigation of underwater robotic vehicles for oceanographic missions under moving sea ice. We define the ice-relative navigation problem and report a novel underwater robot vehicle, Nereid-UI, and its navigation system designed for under-ice oceanographic operations in ice-covered seas. The goal of Nereid-UI is to enable deployments in polar ocean regions traditionally considered difficult or impossible to access such as the ice-ocean interface in marginal ice zones, the water column of ice-covered seas, and the water column underlying ice shelves. This paper also reports the navigation results of Nereid-UI's first under-ice sea trials and science operations conducted under moving multi-year sea ice off the ice breaker Polarstern in the Arctic Ocean at 83° N 6° W - about 200 km NE of Greenland.

Survey class Autonomous Underwater Vehicles (AUVs) rely on Doppler Velocity Logs (DVL) for precise navigation near the seafloor. In cases where the seafloor depth is greater than the DVL bottom lock range, transiting from the surface where GPS is available to the seafloor presents a localisation problem since both GPS and DVL are unavailable in the mid-water column. This is traditionally addressed by using acoustic positioning systems, which take extra time to deploy or require a tracking vessel. Such systems increase the costs of operating in deep waters and reduce the flexibility of AUV operations. This paper proposes an alternative approach to navigation in the mid-water column that exploits the stability of current profiles of water columns over short periods of time. Observation of these currents are possible with the ADCP (Acoustic Doppler Current Profiler) mode of the DVL. Results with real data from missions with the Sirius AUV show how the full integration of water column descent with the ADCP, seafloor view-based SLAM (Simultaneous Localisation And Mapping), and ascent to the sea surface with ADCP gives results similar to having continuous bottom lock and shows potential to act as an alternative to acoustic localisation.

This paper presents an efficient and featureless approach to Bathymetric Simultaneous Localization And Mapping (SLAM) that utilizes a Rao-Blackwellized Particle Filter (RBPF) and Gaussian Process (GP) Regression to provide loop closures in areas where little to no overlap with previously explored terrain is present. To significantly reduce the memory requirements of this approach (thereby allowing for the processing of large datasets) a novel map representation is also introduced that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronizes them to a common log of bathymetric observations. Upon detecting a loop closure each particle is then weighted by matching new observations to the current predictions generated from a local reconstruction of their map using GP Regression. Here the spatial correlation in the environment is fully exploited, allowing predictions of seabed depth to be generated in areas that may not have been directly observed previously. The particle resampling that is performed therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. The results demonstrate how observations of seafloor structure with partial overlap can be used by bathymetric SLAM to improve map self consistency when compared to Dead Reckoning fused with Long-Baseline observations. In addition we show how mapping corrections can still be achieved even when no map overlap is present.

Autonomous underwater vehicles (AUVs) are commonly used to support oceanographic science by providing water-column mapping, seafloor bathymetric and photographic survey, and deep-sea exploration capabilities. In practice, the mapping activities carried out by AUVs consist of flying either pre-programmed tracklines (most propeller-driven AUVs), or else reporting data to human operators at regular intervals that permit retasking (typical for month-long underwater glider deployments). AUVs equipped with the ability to reason about scientific objectives in real time could significantly increase the value of individual deployments by enabling sampling efforts to be focused on targets or areas identified autonomously or semi-autonomously as scientifically interesting [1]. In this paper, we focus on AUV autonomy as it pertains to water-column sensing and argue that the classification of water-column sensor data represents an important enabling capability. We demonstrate practical, semi-supervised classification of water-column sensor data using a particular Bayesian, non-parametric clustering method, the Variational Dirichlet Process, combined with operator-supplied semantic labeling. The method is applied to the detection of a deep subsea hydrocarbon plume using data collected by the Woods Hole Oceanographic's Sentry AUV during an expedition to the Gulf of Mexico following the Deepwater Horizon blowout disaster.

This paper reports the Sentry autonomous underwater vehicle and its deployment on two cruises in response to the Deepwater Horizon oil spill. The first cruise, in June 2010, coupled Sentry with the TETHYS mass spectrometer to track and localize a subsea hydrocarbon plume at a depth of approximately 1100m going at least 30km from the oil spill site. In December 2010, Sentry mapped and photographed deep-sea biological communities for follow-up observations and sampling with the Alvin manned submersible. These cruises demonstrate how robots and novel sensing technologies contributed to the oil spill assessment and the broader impact of technologies developed for basic research.

This paper reports an overview of the navigation and control system design for the new Nereus hybrid underwater robotic vehicle (HROV). Vehicle performance during its first sea trials in November 2007 near Hawaii, and in May and June 2009 in the Challenger Deep of the Mariana Trench is reported. During the latter expedition, the vehicle successfully performed scientific observation and sampling operations at depths exceeding 10,903 m. The Nereus underwater vehicle is designed to perform scientific survey and sampling to the full depth of the ocean - significantly deeper than the depth capability of all other present-day operational vehicles. For comparison, the second deepest underwater vehicle currently operational worldwide can dive to 7,000 m maximum depth. Nereus operates in two different modes. For broad-area survey, the vehicle can operate untethered as an autonomous underwater vehicle (AUV) capable of exploring and mapping the sea floor with sonars and cameras. Nereus can be converted at sea to become a tethered remotely operated vehicle (ROV) to enable close-up imaging and sampling. The ROV configuration incorporates a lightweight fiber-optic tether (for high-bandwidth, real-time video and data telemetry to the surface), an electro-hydraulic manipulator arm, and sampling instruments. The Nereus vehicle is designed to render all parts of the Earth's seafloor accessible to oceanographic science.

This paper describes an approach to achieving high resolution, repeated benthic surveying using an Autonomous Underwater Vehicle (AUV). A stereo based Simultaneous Localisation and Mapping (SLAM) technique is used to estimate the trajectory of the vehicle during multiple overlapping grid based surveys. The vehicle begins each dive on the surface and uses GPS to navigate to a designated start location. Once it reaches the designated location on the surface, the vehicle dives and executes a pre-programmed grid survey, collecting co-registered high resolution stereo images, multibeam sonar and water chemistry data. A suite of navigation instruments are used while the vehicle is underway to estimate its pose relative to the local navigation frame. Following recovery of the vehicle, the SLAM technique is used to refine the estimated vehicle trajectory and to find loop closures both within each survey and between successive missions to co-register the dives. Results are presented from recent deployments of the AUV Sirius at a site in North Eastern Tasmania. The objective of the deployments described in this work were to document the behaviour of barrens-forming sea sea urchins which have recently become resident in the area. The sea urchins can overgraze luxuriant kelp beds that once dominated these areas, leaving only rocky barrens habitat. The high resolution stereo images and resulting three dimensional surface models allow the nocturnal behaviour of the animals, which emerge to feed predominantly at night, to be described. Co-registered images and resulting habitat models collected during the day and at night are being analysed to describe the behaviour of the sea urchins in more detail.

Robotic agents that can explore and sample in a completely unsupervised fashion could greatly increase the amount of scientific data gathered in dangerous and inaccessible environments. Our application is imaging the benthos using an autonomous underwater vehicle with limited communication to surface craft. Robotic exploration of this nature demands in situ data analysis. To this end, this paper presents results of using a Gaussian Mixture Model (GMM), a Hidden Markov Model (HMM) filter, an Infinite Gaussian Mixture Model (IGMM) and a Variation Dirichlet Process model (VDP) for the classification of benthic habitats. All of the models are trained using unsupervised methods. Furthermore, the IGMM and VDP are trained without knowing the the number of classes in the dataset. It was found that the sequential information the HMM filter provides to the classification process adds lag to the habitat boundary estimates, reducing the classification accuracy. The VDP proved to be the most accurate classifier of the four tested, and also one of the fastest to train. We conclude that the VDP is a powerful model for entirely autonomous labelling of benthic datasets.

In this paper we propose an approach to SLAM suitable for bathymetric mapping by an autonomous underwater vehicle (AUV). AUVs typically do not have access to GPS while underway and the survey areas of interest are unlikely to contain features that can easily be identified and tracked using bathymetric sonar. We demonstrate how the uncertainty in the vehicle state can be modeled using a particle filter and an Extended Kalman Filter (EKF), where each particle maintains a 2D depth map to model the seafloor. Efficient methods for maintaining and resampling the joint maps and particles using Distributed Particle Mapping are then described. Our algorithm was tested using field data collected by an AUV equipped with multibeam sonar. The results achieved by Bathymetric distributed Particle SLAM (BPSLAM) demonstrate how observations of the seafloor structure improve the estimated trajectory and resulting map when compared to dead reckoning fused with USBL observations, the best navigation solution during the trials. Furthermore, the computational run time to deliver these results falls well below the total mission time, providing the potential for the algorithm to be implemented in real time.

In this paper we address the problem of localizing active hydrothermal vents on the seafloor using an autonomous underwater vehicle (AUV). Woods Hole Oceanographic Institution's Autonomous Benthic Explorer (ABE) (see Fig.1) AUV has been successfully used in several hydrothermal vents prospecting missions. Recently, a three-stage nested approach [1] has been introduced. It consists in surveys at different altitudes to finally photograph the venting structures flying very close to the seafloor. In this paper, we introduce a new strategy for ABE's movement in surveys at higher altitude (phase-1): ABE moves along predesigned tracklines sampling the seawater; when some conditions on collected data are encountered, it starts a spiral movement to fly over areas likely to contain active vent fields. Results of the proposed algorithm tested on data coming from previous ABE's missions are shown and assess the efficiency and reliability of the method.

Abstract — The Arctic seafloor remains one of the last unexplored areas on Earth. Exploration of this unique environment using standard remotely operated oceanographic tools has been obstructed by the dense Arctic ice cover. In the summer of 2007 the Arctic Gakkel Vents Expedition (AGAVE) was conducted with the express intention of understanding aspects of the marine biology, chemistry and geology associated with hydrothermal venting on the section of the mid-ocean ridge known as the Gakkel Ridge. Unlike previous research expeditions to the Arctic the focus was on high resolution imaging and sampling of the deep seafloor. To accomplish our goals we designed two new Autonomous Underwater Vehicles (AUVs) named Jaguar and Puma, which performed a total of nine dives at depths of up to 4062m. These AUVs were used in combination with a towed vehicle and a conventional CTD (conductivity, temperature and depth) program to characterize the seafloor. This paper describes the design decisions and operational changes required to ensure useful service, and facilitate deployment, operation, and recovery in the unique Arctic environment. I.

This paper addresses the problem of autonomous localization of multiple gas or odor sources in an indoor environment with no strong airflow. In our approach, a robot iteratively builds an occupancy grid map from successive measurements of odor concentration. The resulting map shows the probability of each discrete cell in the map containing an active plume source. Our method is based on a recent adaptation of Bayesian occupancy grid mapping (OGM) to the chemical plume source localization problem. We present experimental results that demonstrate the utility of the approach.